System and method for custom material, replacement

ABSTRACT

A method for generating a material for a partial replacement includes receiving an image of a sample of an original material needing to be replaced, generating a representative data set for the original material based on the image received, accessing a reference database comprised of a plurality of different materials and including reference texture data or reference characteristic data for each of the plurality of different materials, comparing the representative data set to the reference data in the reference database, identifying at least one of the plurality of different materials correlated to the representative data set, determining the identified correlated material is not available, sending a request to generate a replacement material, and generating the replacement material.

FIELD OF THE DISCLOSURE

This disclosure generally relates to replacement of materials and, moreparticularly, to generating custom material as a replacement for damagedmaterials to match discontinued and/or weathered construction materialsduring a claims process between an insured and the insurance company.

BACKGROUND

During a claim handling process, insurance companies must weigh variousfactors to determine the most efficient method to replace and/or repairlosses by a policy holder. These factors apply whether the loss is to avehicle, a building, or any other insured item. For example, if the hoodof an automobile is damaged, relevant factors may include the cost of areplacement hood, the cost of repairing the original hood, whether areplacement hood is available in a timely manner, the difficulty ofmatching the paint for the replacement hood with that on the rest of theautomobile, and more. If the cost of repairing the damaged portion of avehicle exceeds certain thresholds, the insurance company may “total”the vehicle and provide the customer with a payment of replacement valueas determined by the terms of the relevant insurance policy.

In the case of damaged roofing, siding, and/or other material on a houseor building, it is typically more cost-effective to replace only thedamaged portion rather than the entire roof, all of the siding, etc.This option (called “partial replacement”) may not be available,however, if the particular brand/model of material is no longer beingproduced or the original material is so aged and/or weathered that abrand-new section of the same material will not match. For these andother reasons, the policy holder may prefer a complete replacement to apartial replacement.

SUMMARY

This summary is provided to introduce teachings of the presentdisclosure in a simplified form. The teachings are not limited to thissummary, nor should the summary be read to limit the scope of theclaimed subject matter.

In one embodiment, a method for generating a material for a partialreplacement comprises receiving an image of a sample of an originalmaterial needing to be replaced, generating a representative data setfor the original material based on the image received, accessing areference database comprised of a plurality of different materials andincluding reference texture data or reference characteristic data foreach of the plurality of different materials, comparing therepresentative data set to the reference data in the reference database,identifying at least one of the plurality of different materialscorrelated to the representative data set, determining the identifiedcorrelated material is not available, sending a request to generate areplacement material, and generating the replacement material.

In another embodiment, a system for generating a replacement material ata custom manufacturing facility comprising a processor, a non-transitorystorage medium, and a set of computer readable instructions stored inthe non-transitory storage medium and, when executed by the processor,configured to receive an image of a sample of an original materialneeding to be replaced, generate a representative data set for theoriginal material based on the image received, access a referencedatabase comprised of a plurality of different materials and includingreference texture data or reference characteristic data for each of theplurality of different materials, compare the representative data set tothe reference data in the reference database, identify at least one ofthe plurality of different materials correlated to the representativedata set, determine the identified correlated material is not available,and send a request to the custom manufacturing facility to generate areplacement material.

In yet a further embodiment, a method for generating a replacementmaterial comprises providing a claims handling application to a user forinstallation on an information handling device, and collecting claimsdata at an insurance company server, the claims data transmitted by theinformation handling device through the claims handling application tothe server. The server comprises a processor and a memory that storesthe claims data, wherein the server receives an image of a sample of anoriginal material needing to be replaced, generates a representativedata set for the original material based on the image received, accessesa reference database comprised of a plurality of different materials andincluding reference texture data or reference characteristic data foreach of the plurality of different materials, compares therepresentative data set to the reference data in the reference database,identifies at least one of the plurality of different materialscorrelated to the representative data set, determines the identifiedcorrelated material is not available, and sends a request to a custommanufacturing facility to generate a replacement material.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrating an illustrative method for replacingdamaged materials according to teachings of the present disclosure.

FIG. 2 is an illustrative system for identifying construction materialsaccording to teachings of the present disclosure.

FIG. 3 is an illustrative database for use with the teachings of thepresent disclosure.

FIG. 4 is a flowchart of an illustrative method for analyzing a queryregarding replacement material according to various aspects of thepresent disclosure.

FIG. 5 is a flowchart of an illustrative method for identifying amaterial sample by comparison to reference samples using a referencedatabase according to various aspects of the present disclosure.

FIGS. 6A-6G are drawings showing example screenshots that might begenerated by an application for display on a user's mobile device,according to teachings of the present disclosure.

FIG. 7 is an illustrative system for executing the methods of at leastFIGS. 4 and 5 .

FIG. 8 is an illustrative mobile device for use with the teachings ofthe present disclosure.

Corresponding reference characters indicate corresponding partsthroughout the several views. Although the drawings representembodiments of various features and components according to the presentdisclosure, the drawings are not necessarily to scale and certainfeatures may be exaggerated in order to better illustrate and explainthe present disclosure. The exemplifications set out herein illustrateembodiments of the disclosure, and such exemplifications are not to beconstrued as limiting the scope of the disclosure in any manner.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principals of thedisclosure, reference will now be made to the embodiments illustrated inthe drawings, which are described below. The embodiments disclosed beloware not intended to be exhaustive or limit the disclosure to the preciseform disclosed in the following detailed description. Rather, theembodiments are chosen and described so that others skilled in the artmay utilize their teachings. It will be understood that no limitation ofthe scope of the disclosure is thereby intended. The disclosure includesany alterations and further modifications in the illustrative devicesand described methods and further applications of the principles of thedisclosure which would normally occur to one skilled in the art to whichthe disclosure relates.

Known methods for settling claims include multiple visits, calls, orother contact from and/or to adjusters, repair shops, contractors, etc.If options for a partial replacement are difficult to ascertain ortime-consuming for the policy holder, the claims process may be evenmore frustrating than the original loss. A quick and efficient systemfor providing replacement materials may increase customer satisfactionas well as reduce costs related to insured losses.

Exterior siding (e.g., on a home or other building), roofing materials,window sills, window trim, plumbing fixtures, plumbing joints, plumbingmaterials, flooring, countertops, and other construction materials havepresented difficulty for repair projects. It may be difficult toidentify the original material used in construction. In some cases, theoriginal material may be weathered and/or otherwise aged to the extentthat new replacement material will not match. In other cases, theoriginal material may be out of production or unavailable in anacceptable time frame. In any of these situations, current methods forsettling claims may result in a complete replacement of both the damagedportion of the materials as well as undamaged material, what may becalled a “complete replacement.”

Additionally, incorrect identification of the original constructionmaterial may lead to delays and/or increased expenses. For example,replacement material may be delivered and installed but not sufficientlymatch the existing material to the satisfaction of the policy holder(e.g., pattern, texture, color, etc.). Removal, return, and/orreplacement of the replacement material(s) adds time and cost to arepair process.

Targeted partial replacements of construction materials may providesavings in time, cost, and other factors for both an insurance companyand the policy holder. The teachings of the present disclosure providemethods and systems allowing efficient use of targeted partialreplacements for insurance companies and policy holders, whether fordamaged buildings, vehicles, or other insured losses.

Embodiments of the present disclosure and their advantages over theprior art may be best understood by reference to the Figures describedbelow.

FIG. 1 is a flowchart illustrating an example method 100 for replacingdamaged materials according to teachings of the present disclosure.Example method 100 may include various steps illustrated in FIG. 1 , butthe order of the steps may be varied without departing from the scope ofthe present disclosure. In addition, persons having ordinary skill inthe art will be able to add or remove steps without departing from thescope. In general, if a customer suffers a loss and/or damage to itsproperty, then a service provider may facilitate repair and/orreplacement of the customer's loss. Method 100 may be appropriate formany types of loss, not limited to those which implicate the need forcustom manufacturing of replacement material.

At Step 102, method 100 begins with a customer realizing a loss ordamage to its property and starting the repair process. In someexamples, this may be an insured loss, such as damage to siding orroofing material on a building or house. In some embodiments, there mayor may not be an insurance policy related to the loss, but one or moreservice representatives may provide assistance to the customer through aloss repair application or request process. The one or more servicerepresentatives may be employed by a repair service company, aninsurance company, and/or any other service provider. In someembodiments, method 100 may be a service available by subscriptionand/or pay-per-use.

At Step 104, the customer may open a request. Opening a request mayinclude contacting the service provider by telephone, email, and/or anyother appropriate means. In some embodiments, the customer will use aweb-based contact form or an application running on the customer'smobile device (e.g., a smart phone, a laptop/notebook computer, etc.).

The request may include any customer data that may facilitate providingservice. For example, customer data may include customer identificationinformation (e.g., customer name and contact information). Customer datamay include information related to the loss, including identification ofthe loss and/or damaged property. When an insurance company is theservice provider, the customer data may include a policy number and/orother data identifying the relationship between the customer and theinsurance company. As another example, if the service provider is a homewarranty and/or construction warranty company, the customer data mayidentify a warranty contract and/or other relationship between thecustomer and the service provider.

At Step 106, the customer may elect whether to request assistance from arepresentative (a “rep” or “claim rep”). In embodiments employed by aninsurance company, customers may be familiar and/or comfortable and,therefore, prefer to proceed with a claim representative to manage aclaim for repair to home damage and/or vehicle damage. In otherembodiments, a customer may have the option to use a repairrepresentative to facilitate the process even if there is no insuredloss. If the customer elects to proceed with a “self-service” claim,method 100 proceeds to Step 108. If the customer elects to proceed withassistance of a representative, method 100 moves to Step 110.

At Step 108, a representative of the service provider investigates theclaim and the damaged material(s). Step 108 may include taking one ormore images of the damaged material and/or the nearby undamagedmaterials. In the example of siding damage, Step 108 may include takinga photograph of the wall and/or the damaged area. Step 108 may includetaking a rubbing of the undamaged siding to allow analysis of thetexture of the siding. In other examples, Step 108 may include an imageof other identifying marks and/or features of a damaged material (e.g.,the VIN of a damaged vehicle, the paint color of a damaged vehicle, thecolor and/or texture of roofing material, etc.). The one or more imagesmay be captured using a mobile device (e.g., a smartphone, a camera,etc.). Alternatively, the representative may remotely obtain such imagesusing unmanned aerial vehicles (“UAVs” or drones), self-driving carswith cameras, other manners of providing a camera to the location of thedamage, or with cameras already present at the location of the damage(e.g., security cameras, neighborhood cameras, etc.).

At Step 110, if the customer elects, he or she may capture one or moreimages showing the damage and/or the nearby undamaged materials. Thevarious aspects of capturing the image may be similar to those of Step108. In embodiments providing an application running on the customers'mobile device, the application may provide a user interface forcapturing the images.

Both Step 108 and Step 110 may further include sending the one or moreimages to the service provider. This may include uploading an image froma mobile device to email and/or to the provider's website. Inembodiments using an application running on a mobile device, theapplication may provide a user interface for sending the images.

Step 112 may include identifying the damaged material. In someembodiments, the service provider may host a database holdinginformation related to various materials, including constructionmaterials, vehicle manufacturing materials, etc. The discussion of FIG.2 below provides a detailed description of one embodiment of a systemfor identifying a particular construction material.

At Step 114, the service provider determines whether the identifiedmaterial has an available replacement. For example, the particularmaterial may be out of production. In another example, although theoriginal material is still available, the surrounding, undamagedmaterial may be so weathered that replacing the damaged material withbrand new replacement material is not a satisfactory option. Step 114may include a request to the original manufacturer and/or a search ofmaterial suppliers to determine inventory, time to fill an order, etc.

If the identified replacement material is available and matches theremaining undamaged material, method 100 may proceed to Step 116. As anexample, a damaged portion of an automobile may be available forpurchase and installation without challenge. As another example, roofingshingles may be available that match the current roofing materials andrepairs may proceed by purchasing an appropriate quantity of saidroofing shingles. At Step 116, the requested repairs may be performedusing any appropriate methods and/or systems.

After the requested repairs are complete in Step 116, method 100 mayterminate at Step 118.

If, however, the potential replacement is not available and/or doesn'tsufficiently match the remaining materials, method 100 may proceed toStep 120. At Step 120, the service provider may determine whether to doa full replacement or a partial replacement. As an example, if theservice provider determines 75% of a particular roof is damaged, it maybe cheaper to replace the entire roof rather than to buy the specificroofing material required to replace the damaged portion(s). In anotherexample, the cost to acquire a sufficient amount of replacement materialmatching the undamaged portion may be more expensive than replacing theentire roof with an equivalent material.

If the service provider determines a full replacement is appropriate,method 100 proceeds to Step 122. At Step 122, the requested replacementmay be performed using any appropriate methods and/or systems. Forexample, the service provider may contract with a roofing company toreplace the entire roof, with a siding company to replace the siding onan entire building and/or house, etc. Following Step 122, at Step 124,method 100 is complete.

If, on the other hand, the service provider determines that a partialreplacement is the better option at Step 120, method 100 may proceed toStep 130. At Step 130, the service provider may request custommanufacturing of replacement material. Step 130 may include sending anyrelevant information to a custom manufacturing facility. The relevantinformation may include identification of the material by manufacturer,brand, model, etc. and a requested amount of material needed to completethe partial replacement. The service provider may provide manufacturingspecifications as appropriate, including textures, material selection,etc. In some embodiments, the service provider may provide specificmanufacturing guidelines and/or computer (e.g., CAD or engineeringdrawing) files for use by the manufacturing facility.

In some embodiments of method 100, the service provider may own and/oroperate a custom manufacturing facility. In these embodiments, Step 130may include providing an internal work order from the representativeand/or an app-based system to the provider's manufacturing facility.

At Step 132, the custom manufacturing facility may produce thereplacement material(s) as identified in Step 130 by the manufacturingrequest. Custom manufacturing may include any known systems formanufacturing replacement materials, including roofing, siding, plumbingmaterials and/or fixtures, automotive parts, etc. For example, custommanufacturing may include additive manufacturing (sometimes called 3Dprinting, desktop manufacturing, and/or rapid manufacturing), referringto any process for laying successive layers of material to build a 3Dobject including, for example, selective laser sintering, selectivelaser melting, laminated object manufacturing, fused filamentfabrication, fused deposition, etc. In some embodiments, additivemanufacturing may be used to produce an entire replacement material. Inother embodiments, additive manufacturing may be used to provide afinishing layer or technique to existing materials, for example additivemanufacturing may be used to produce a veneer or other outer layer on anexisting material. Other manufacturing methods in addition to 3Dprinting may include form pressing or the like. Additionally, any ofthese manufacturing methods or processes may include any types ofmaterials, plastics, quartz, ceramics, etc., for example various sidingpanels may be formed through 3D printing but then coated or otherwiseadded to through other methods and using other materials.

The various custom manufacturing processes may allow a service providerto match materials that are no longer in production from the originalmanufacturer. In other examples, custom manufacturing may allow theservice provider to match weathered and/or aged materials that no longermatch the features of the materials as originally manufactured. Thesecustom manufacturing processes may allow the service provider to executea partial replacement of damaged materials, and therefore provide morecost-effective service, compared to available conventional repairprocesses.

At Step 134, the custom manufacturing facility may provide the customreplacement materials to an appropriate destination. For example, if thedamaged materials are construction materials for a house, thereplacement materials may be shipped to the home for installation. Asanother example, if the damaged materials are part of a vehicle, thereplacement materials may be shipped to a body shop and/or repair shopfor installation.

At Step 136, the service provider may request the customer assess thecustom replacement materials to approve the match and/or other qualityof the material before it is installed. The service provider may allowthe customer to approve the replacement material rather than risk adissatisfied customer and/or the need to replace the replacementmaterials. Additionally or alternatively, the service provider mayassess the replacement materials and provide approval beforeinstallation.

Assuming the replacement material is approved at Step 136, the processmay continue by conducting the repairs. The requested repairs may beperformed using any appropriate methods and/or systems. For example, theservice provider may contract with a roofing company to replace theentire roof, with a siding company to replace the siding on an entirebuilding and/or house, etc.

At Step 138, method 100 is complete.

FIG. 2 is a drawing illustrating an example system 200 for identifyingconstruction materials according to teachings of the present disclosure.In general, system 200 may be operable to identify one or moresubstantially similar products for unknown materials, e.g., a sample ofsiding to be replaced on a building having old or damaged siding or asample of roofing materials. As shown, system 200 may include a digitalimage generator 212, an analysis engine 214, a reference database 216, auser input device 228, and a display device 230.

Digital image generator 212 may comprise any system or device configuredto generate one or more digital images 224 of a material sample 220 or amaterial sample representation 222. As used herein, “digital images” mayinclude image data or other digital data representative of the physicalsurface and/or appearance of a material sample or sample representation.For example, digital image generator 212 may be configured to generate adigital photographic image, a digital scanned image, or other digitaldata representative of a material sample 220 and/or a material samplerepresentation 222. Thus, digital image generator 212 may be, forexample, (a) a digital camera (e.g., a stand-alone camera or a cameraincorporated in another device, such as a smart phone or personaldigital assistant, for example), (b) a scanner (e.g., a flatbed scanner,photo scanner, sheet-fed scanner, portable handheld scanner, or ascanner incorporated in another device, such as a smart phone orpersonal digital assistant, for example), (c) a Light Detection andRanging (LIDAR) system or device, (d) a digital elevation modeling (DEM)system or device, (e) a quality control surface inspection system ordevice, or any other device configured to generate digital data orimages. As used herein, the term “sample” refers to any physical pieceof the relevant material, or any portion thereof, e.g., a board, ashingle, a sheet of siding, any piece of the material to be replaced, orany portion thereof. A sample may be formed of any suitable type ofmaterial, e.g., vinyl, HardiPlank™, metal, or composite materials.

A “sample representation” may include any tangible representation of asample. Examples of sample representations include: (a) a transferredink image (e.g., an ink rubbing) of a sample (discussed below); and (b)a tangible image of a siding sample, e.g., a printed photographic orprinted image of a sample.

Each digital image 224 of a sample 220 or a sample representation 222 ofa sample 220 may include data regarding the physical texture of thesample 220. For example, the digital image 224 may indicate texturalfeatures such as grooves, recesses, protrusions, or other dimensionalfeatures that simulate wood grain, for example.

Analysis engine 214 may be configured to analyze data regarding aparticular sample, referred to as a “query sample,” to identify orattempt to identify the type of the query sample. For example, in someembodiments, analysis engine 214 may receive: (a) one or more digitalimages 224 of the query sample (e.g., a “query image”) received fromdigital image generator 212; and/or (b) additional characteristic data226 regarding the query sample. Additional characteristic data 226 maybe accessed by analysis engine 214 from any suitable source in anysuitable manner. For example, characteristic data 226 may be manuallyinput by a user at user input device 228, automatically accessed fromuser input device 228 or other data source, or some combination of thetwo (e.g., in response to receiving particular characteristic data 226input by a user, analysis engine 214 may automatically access furthercharacteristic data 226 from user input device 228 or other data sourcebased on the data input by the user).

Referring to FIGS. 2 and 3 , additional characteristic data 226 mayinclude texture-related data and non-texture-related data regarding thequery sample. For example, additional characteristic data 226 mayinclude any or all of the following categories of data: (a) Material,(b) Style, (c) Face Size, (d) Profile, (e) Manufacturer, (f) Butt Size,(g) Thickness, (h) Weep Hole Shapes, (i) Nail Hem Shapes, (j) WaterMarks, (k) Knot Designs, (l) Color, and/or any other categories. Each ofthese example categories is discussed in detail below with reference toFIG. 3 .

As shown in FIG. 2 , analysis engine 214 may include a processor 232configured to execute instructions 236 stored in memory 234 forproviding any of the various functionality of analysis engine 214.Processor 232 may include a microprocessor, a microcontroller, a digitalsignal processor (DSP), an application specific integrated controller(ASIC), electrically-programmable read-only memory (EPROM), or afield-programmable gate array (FPGA), or any other suitableprocessor(s), and may be generally operable to execute instructions 236stored in memory 234. Instructions 236 may include any form ofcomputer-readable instructions or code, e.g., one or more algorithms orapplications. In some embodiments, instructions 236 include a analysisapplication configured to analyze query samples and provide userinteraction with engine 214.

In some embodiments, analysis engine 214, user input device 228, anddisplay 230 are communicatively coupled such that analysis engine 214displays data on display 230, which may include user interface screensallowing a user to interact with analysis engine 214 via user inputdevice 228, e.g., to enter query sample characteristic data 226, selectother search parameters, view search results from engine 214, etc.Example screen shots generated by analysis engine 214 for allowing auser to interface with engine 214 via user input device 228 are providedin FIGS. 6A-6J, which are discussed below.

Memory 234 may store executable instructions 236 (e.g., algorithms 238)related to the operation of analysis engine 214. Memory 234 may compriseany one or more devices suitable for storing electronic data, e.g., RAM,DRAM, ROM, internal flash memory, external flash memory cards (e.g.,Multi Media Card (MMC), Reduced-Size MMC (RS-MMC), Secure Digital (SD),MiniSD, MicroSD, Compact Flash, Ultra Compact Flash, Sony Memory Stick,etc.), SIM memory, and/or any other type of volatile or non-volatilememory or storage device. Instructions 236 may be embodied in anycombination of software, firmware, and/or any other type ofcomputer-readable instructions. For example, instructions 236 may beembodied in the form of an application and/or any suitable plug-ins,readers, viewers, updates, patches, or other code, which may bedownloaded via the Internet or installed on the relevant computer devicein any other known manner.

User input device 228 may comprise any device configured to receiveinstructions and/or input from a user related to identification system210. For example, user input device 228 may provide a user interface forcontrolling the operation of analysis engine 214 and/or for enteringdata relevant to operation of analysis engine 214 or other components ofidentification system 210, e.g., characteristic data 226 regarding aparticular query image. User input device 228 may include any suitableuser interfaces, e.g., touch screen, keyboard, mouse, physical buttons,or any other suitable devices. In some embodiments, user input device228 may include a mobile computing device such as a smartphone or aportable computer.

Display 230 may comprise any type of display device for displayinginformation related to identification system 210, such as for example, amonitor, LCD screen, or any other suitable type of display. In someembodiments, display 230 may be an interactive display (e.g., a touchscreen) that allows a user to interact with identification system 210.In other embodiments, display 230 may be strictly a display device, suchthat all user input is received via user input device 228.

Reference database 216 may comprise any suitable database for storingreference data 250, and may be stored in any suitable memory device onany suitable computer (e.g., server, desktop computer, laptop computer,tablet-style computer, smartphone, PDA, etc.). In some embodiments,reference database 216 may be stored in memory 234, while in otherembodiments reference database 216 may be stored separately and/orremotely from analysis engine 214.

As discussed in greater detail below with reference to FIG. 3 ,reference data 250 stored in reference database 216 may include variousdata related to any number of different reference samples 252. Analysisengine 214 may compare one or more query image(s) 224 and/or additionalcharacteristic data 226 for an unknown material sample with suchreference data 250 to identify one or more reference material samples252 that match the unknown siding sample, in order to identify theunknown siding sample.

Depending on the particular embodiment, any or all of digital imagegenerator 212, analysis engine 214, reference database 216, user inputdevice 228, and display 230 may be integral with each other, or may bedistinct from each other, in any suitable combination. For example, insome embodiments, all of digital image generator 212, analysis engine214, reference database 216, user input device 228, and display 230 maybe integrated in a single device, e.g., desktop computer, laptopcomputer, tablet-style computer, smartphone, personal digital assistant(PDA), or any other suitable electronics device. In other embodiments,digital image generator 212, analysis engine 214, reference database216, user input device 228, and display 230 may all be separate devices,some or all of which may be connected by a network or other suitablecommunication links.

In other embodiments, analysis engine 214, reference database 216, userinput device 228, and display 230 may be integrated in a single device(e.g., desktop computer, laptop computer, tablet-style computer,smartphone, PDA, or any other suitable electronics device), whiledigital image generator 212 is a separate device (e.g., a scanner,camera, or other image generator, e.g., provided as a stand-alone-deviceor provided by a laptop, smartphone, or PDA, for example). For example,a user may carry digital image generator 212 (e.g., in the form of ascanner, laptop, or smartphone) to a location of a sample 220 to beidentified, use digital image generator 212 to generate a digital image224 of the material sample 220 and/or of a sample representation 222(e.g., a transferred ink image) of the sample 220 and then communicateor transfer the digital image 224 to analysis engine 214 (e.g., via anysuitable communications links using any suitable communicationsprotocols, or by physically transferring the digital image 224 using aUSB drive, or in any other suitable manner).

In other embodiments, digital image generator 212 and user input device228 may be integrated in a first device (e.g., desktop computer, laptopcomputer, tablet-style computer, smartphone, PDA, or any other suitableelectronics device), while analysis engine 214, reference database 216,and display 230 are integrated in a second device (e.g., desktopcomputer, laptop computer, tablet-style computer, smartphone, PDA, orany other suitable electronics device). Alternatively, referencedatabase 216 may be provided by a third device, separate and/or remotefrom analysis engine 214.

In other embodiments, digital image generator 212, user input device228, and device 230 may be integrated in a first device (e.g., desktopcomputer, laptop computer, tablet-style computer, smartphone, PDA, orany other suitable electronics device), while analysis engine 214 andreference database 216 are integrated in a second device (e.g., desktopcomputer, laptop computer, tablet-style computer, smartphone, PDA, orany other suitable electronics device).

In other embodiments, digital image generator 212, user input device228, analysis engine 214, and display 230 may be integrated in a firstdevice (e.g., desktop computer, laptop computer, tablet-style computer,smartphone, PDA, or any other suitable electronics device), whilereference database 216 is provided in a separate second device (e.g., ina separate server or other computer) remote from the first device. Insuch embodiments, the first device may communicate with the seconddevice via any suitable communications links using any suitablecommunications protocols, e.g., to allow analysis engine 214 to accessreference data 250 for analyzing a query image 224.

Embodiments may include any suitable configuration of identificationsystem 10.

FIG. 3 is a drawing illustrating an example database 216 storingreference data 250 for use with the teachings of the present disclosure.Reference data 250 may include a number of reference sample data sets252 (indicated as data sets 252 a, 252 b . . . 252 n), eachcorresponding to a different reference sample. Such data may be accessedand used by analysis engine 214 for identifying or finding a similarproduct to the unknown material sample. More particularly, analysisengine 214 may access reference data 250 from reference database 216 andcompare the digital image(s) 224 and/or additional characteristic data226 regarding the query sample with reference data 250. Reference data250 includes reference samples 252 to identify or “match” the querysample, referred to herein as “matching” samples. Thus, for example, amatching sample may be selected for replacing a damaged section ofmaterial corresponding to the query sample.

As used herein, material samples “match” if they are visually identicalor substantially similar. In some embodiments, samples are“substantially similar” if a quantitative measure resulting from apattern recognition analysis of the material samples (e.g., the“similarity score” discussed below) exceeds a predefined threshold, asdisclosed further with respect to FIG. 5 . For example, in oneembodiment, each reference sample having a similarity score of about 70with respect to a query sample is considered substantially similar tothe query sample, and thus is identified as a “matching” sample. Inother embodiments, “substantially similar” may be defined as the “n”most visually similar samples with respect to a particular sample (e.g.,query sample), as determined by a fully or partially automated patternrecognition analysis, where n is any predefined number (e.g., 3, 4, 5,6, 8, 10, etc.). For example, in an embodiment in which analysis engine214 displays the five most similar reference samples to a query sample(e.g., the five reference samples having the highest similarity scorewith respect to the query sample), such five reference samples areconsidered substantially similar to the query sample, and thus areconsidered to be “matching” samples. In other embodiments, substantialsimilarity may be defined in any other suitable manner.

In the illustrated example of FIG. 3 , each reference sample data set252 may include one or more images 260, quantified visual features 264,and/or additional characteristic data 270. Image(s) 260 may comprise oneor more digital images of a material sample or sample representation,e.g., a single image of a sample/sample representation or multipleimages of different portions of a sample/sample representation tocapture different textural patterns at different locations of thesample.

Quantified visual features 264 may include any features and/or featurecharacteristic that may be extracted and/or analyzed from an image 260using any suitable image processing or pattern recognition algorithms,functions, applications, or systems (e.g., as embodied in instructions236). As examples only, quantified visual features 264 may include imagefeatures, e.g., edges, corners, blobs, ridges, lines, curves, shapes,contours, objects, areas, etc. of an image 260 and/or characteristics ofsuch features, e.g., quantity, shape, size, length, width, curvature,direction, orientation, clarity, color, distance between features,feature density, feature clustering, feature distribution, etc., and/orstatistical distributions of any such features, that may be extractedand/or analyzed from the image using any suitable feature extractiontools or techniques, e.g., Gabor filters, local binary patterns (LBP),histogram of oriented gradients (HOG), etc. Similar to Gabor filters,LBP and HOG both reflect some statistical visual property or propertiesof the texture image 260.

Additional characteristic data 270 may include any data regarding anycharacteristics that may be useful in identifying a material sample. Inthis illustrated example, characteristic data 270 may include thefollowing categories of data:

(a) Construction. The “construction” classification may identify thesample. Example “construction” classifications may include: aluminum(solid color), aluminum (variegated), composite, concrete fiber(asbestos), fiber cement, fiberglass, pressed board (Masonite), steel(solid color), steel (variegated color), vinyl (solid color), vinyl(variegated color), wood, etc.

(b) Style. Example “style” classifications may include: asbestosreplacement, board and batten, clapboard, double beaded, dutchlap, fullbead, half bead, half rounds, logs, masonry, panel, shakes,soffit-beaded, soffit-flat panels, soffit-U groove soffit, soffit-Vgroove soffit, vertical, etc.

(c) Face Size. Example “face size” classifications may include: 1″, 1½″,1 11/16″, 1¾″, 2″, 2⅛″, 2¼″, 2⅝″, 2⅔″, 2¾″, 2⅞″, 3″, 3⅛″, 3¼″, 3⅓″, etc.

(d) Profile. Example “profile” classifications may include: D14″, D3½″,D3¾″, D3⅝″, D4¼″, D4¾″, D4″, D5⅛″, . . . D9″, Flat shingle, Q2¼″, etc.“Profile” is the industry nomenclature for the contour that is formedthat shapes a construction material (e.g., siding) and gives it adistinctive look. Various profile classifications may be found inmanufacturers' literature that describes their respective products. Someexamples are found athttp://www.progressivefoam.com/types-of-vinyl-siding,http://imgs.ebuild.com/xCat/ebuildWebB/15?ObjectID=25801&Variant=Original,andhttp://www.dixiehomecrafters.com/blog/types-of-vinyl-siding-horizontal-and-vertical-profiles.

(e) Manufacturer. Example “manufacturer” classifications may include:Alcan, Alcoa Home Exteriors, Allis Chalmers, Alside, Amcraft BuildingProducts, Ashland Davis, Bird Vinyl Products, etc.

(f) Butt Size. The “butt size” classification is a measure of how farthe bottom of a given siding material projects from the exterior wallplane. Example “butt size” classifications may include: ¼″, ⅜″, ½″, ⅝″,¾″, ⅞″, 1″, 1⅛″, 1¼″, etc.

(g) Thickness. The “thickness” classification is a measure of thephysical thickness of the material. For example, for vinyl siding, the“thickness” classifications may range from 0.038″ to 0.48″ in incrementsof 0.02″. Appropriate “thickness” classifications may similarly beprovided for other types of materials, e.g., steel and aluminum siding.

(h) Weep Hole Shapes. Weep holes are an integral aspect of certain typesof siding and other construction materials (e.g., certain vinyl siding)that provides a path for moisture to escape from behind the exteriorcladding. The weep holes are typically located on the bottom or lowerportion of the material when installed. Some manufacturers havedeveloped weep holes in unique shapes. Thus, samples that include weepholes may be assigned to a predetermined weep hole shape. Example “weephole shape” classifications may include “triangular with straightedges,” “triangular with curved edges,” “square,” “non-squarerectangular,” “oval,” “T-shaped,” etc. Further, “weep hole shape”classifications may include a size factor. In addition, oralternatively, characteristic data 270 may include may include one ormore stored images that represent each weep hole shape classification.These images or similar images may be displayed to a user enteringcharacteristic data 226 for a query sample, to help the user identifythe weep hole shape that corresponds to the query sample.

(i) Nail Hem Shapes. Certain types of construction materials include anail hem that provides an area for mechanically fastening the materialto the building and also locks the piece immediately above it. For sometypes of materials, the nail hem is produced with a distinctive designcharacteristic. Thus, certain types of materials can be classifiedaccording to the shape, design, and/or size of the nail hem. Inaddition, or alternatively, characteristic data 270 may include mayinclude one or more stored images that represent each nail hem shapeclassification. These images or similar images may be displayed to auser entering characteristic data 226 for a query material sample, tohelp the user identify the nail hem shape that corresponds to the querysample.

(j) Water Marks. Water marks are lines provided in certain types ofmaterials, typically oriented generally vertically, e.g., at a rightangle to the main texture. Thus, certain types of materials can beclassified according to the shape, size, orientation, or other aspect ofthe water marks.

(k) Knot Designs. Knot designs are areas provided in certain types ofsimulated wood materials (e.g., vinyl siding) that are designed in theshape of knots present in wood boards. Thus, certain types of materialscan be classified according to the shape, size, orientation, or otheraspect of the knot design.

(l) Color. Certain types of materials are manufactured in specificcolors. Thus, these materials can be classified according to color. Forexample, color data for particular material samples (e.g., correspondingto a particular product and manufacturer) may include a list of allpossible colors for the respective material sample, which data may beobtained from the manufacturer, a third party, or otherwise determined.In some embodiments, analysis engine 214 may be programmed to filter thepossible matches for a query sample by comparing the color of the querysample with characteristic data 270, which includes color data, ofreference samples. For example, a color number for the query sample canbe cross matched to the correct manufacturer, product name, and/orcolor. The color of the query sample may be determined in any suitablemanner, e.g., by digital image generator 212, by a spectrophotometer, orin any other suitable manner.

(m) Other. Characteristic data 270 may include any additional and/ordifferent categories of data regarding each reference material sample.

Some or all of the categories of characteristic data 270 for referencesamples 252 may correspond to some or all of the categories ofcharacteristic data 226 regarding the query sample to be identified,such that the reference sample characteristic data 270 and the querysample characteristic data 226 can be analyzed, e.g., to filter thepotential matches of the query sample.

FIG. 4 is a flowchart illustrating an example method 300 for analyzing aquery regarding replacement material using analysis engine 214 toidentify the query material sample, according to various aspects of thepresent disclosure.

At step 302, digital image generator 212 generates one or more digitalimages 224 of a material sample 220 or a sample representation 222.

At step 304, the query image 224 is loaded or communicated to analysisengine 214 or otherwise accessed by analysis engine 214, depending onthe particular arrangement of system 210. In one example embodiment,digital image generator 212 generates a digital photograph of a materialsample 220 and also digitizes a transferred ink image of the sample 220,both of which are loaded to analysis engine 214.

At step 306, a user may enter additional characteristic data 226regarding the query sample, e.g., via user input device 228. In someembodiments, analysis engine 214 may present one or more user interfacescreens to the user that provide the user a software-based interface forselecting or otherwise entering classifications for one or morecategories of characteristic data 226 regarding the query sample, e.g.,any of the example categories discussed above.

At Step 308, analysis engine 314 analyzes query image 224 and/oradditional characteristic data 226 to identify at least one matchingsample(s). Next, at Step 310, analysis engine 314 displays the matchingsample(s) on user input device 228 and/or display 230. During Step 310,the matching sample(s) may be displayed to the customer, the customerservice representative, and/or the facility manufacturing thereplacement materials, depending on the approval required to moveforward with the repair process and other factors.

With respect to Steps 308 and 310, FIG. 5 is a flowchart illustrating anexample method 320 for identifying a material sample by comparison toreference samples using a reference database (e.g., database 216)according to various aspects of the present disclosure. Method 320 maybe used to perform step 308 of method 300 analyzing a query image 224and/or additional characteristic data 226 of a query material sample toidentify one or more matching reference samples, according to an exampleembodiment.

At step 322 of method 320 (FIG. 5 ), analysis engine 214 may detect andsegment out a texture region of the query image 224 for analysis. Theactual image of the material sample may only cover a portion of thequery image 224, and thus analysis engine 214 may identify and isolatethe texture region from the query image 224 using any suitable detectionand segmentation techniques.

At step 324, analysis engine 214 may extract quantified visual features240 (FIG. 2 ) from the texture region, or from each of a number of“patches” of the texture region. A “patch” is a small sub-region of thequery image 224. For example, supposing the texture region is 512×128pixels, analysis engine 214 may crop the texture region into 16sub-regions, or “patches,” of 64×64 pixels each. Patches may begenerated in any suitable manner, e.g., using overlap or other suitabletechniques.

In one embodiment, analysis engine 214 may use a set of Gabor filterswith different frequencies and orientations to extract useful Gaborfeatures from the texture region (or patches of the texture region) ofquery image 224, e.g., extracting edges and edge properties such as edgefrequencies and orientations, for example. The Gabor features maycorrespond to grain lines or other lines or contours of the materialsample. Analysis engine 214 may extract the distances between theidentified Gabor features (e.g., edges or lines), the orientation of thefeatures, the thickness of the features (e.g., edge or line thickness),the average number of features per square inch, and/or any othersuitable characteristic of the identified features. A visualization ofan example Gabor feature extraction is illustrated athttp://www.cs.utah.edu/˜arul/report/node13.html.

At step 326, analysis engine 214 may perform a pattern recognitiontechnique to classify the query sample and/or identify potential matchesfor the query sample based on the visual features 240 extracted from thetexture region (or from different patches of the texture region) of thequery image 224. In some embodiments, analysis engine 214 may analyzevisual features (e.g., Gabor features) extracted from the texture regionto identify potentially matching reference samples 252, based onquantified visual features 264 (e.g., Gabor features) extracted fromreference sample images 260 stored in reference database 216. Thus,analysis engine 214 may compare the quantified visual features extractedfrom each patch of the texture region with the quantified visualfeatures 264 extracted from reference images 260 stored in referencedatabase 216 for different reference material samples, using anysuitable algorithms. This may include comparing the distribution (e.g.,probability distribution) of identified visual features extracted fromthe query texture region with distributions (e.g., probabilitydistributions) of visual features 264 in different reference materialsample data set 52 a-52 n to quantify the level of visual similaritybetween query image 224 and different individual reference samples.

Further, in some embodiments, analysis engine 214 may compare or analyzea particular type of query image 224 with reference to the same type ofreference images 260 (e.g., analysis engine 214 may compare or analyzequantified visual features extracted from a particular type of queryimage 224 with quantified visual features 264 extracted from the sametype of reference images 260). For example, analysis engine 214 maycompare or analyze a digitized transferred ink image 224 of a querysample with reference to digitized transferred ink images of one or morereference samples. As another example, analysis engine 214 may,alternatively or additionally, compare or analyze a digital photographicimage of a query sample with reference to digital photographic images ofreference material samples.

At step 328, based on performing pattern recognition through analysis ofvisual features 264 at step 326, analysis engine 14 may calculate alevel of similarity (e.g., similarity score) between the query textureregion and different reference material samples 252, using any suitablealgorithms or techniques. For example, an algorithm or technique may beused by analysis engine 14 to quantify and/or qualify the number orquality of matching features between reference sample data 252 and thoseof query image 224. Using such techniques, it is possible to develop asimilarity score based on the number/quality of matching features ortextures between query image 224 and reference sample data 252. Thesimilarity score may be calculated based on weight given to certainmatch criteria, the number of matched features or textures, and/or otherdata or information. In embodiments, a predetermined threshold isestablished for defining a match (e.g., a similarity score ofapproximately 70 or higher may indicate a potential match).

Moreover, analysis engine 214 may utilize any suitable patternrecognition algorithms or techniques for performing step 326 and/or step328, e.g., a Gaussian Mixture Model (GMM), a support vector machine(SVM) model, a combination thereof (e.g., Gaussian kernel SVM),k-nearest neighbor (k-NN), Artificial Neural network, or any othersuitable pattern recognition algorithms or techniques. For example,analysis engine 214 may use one or more of such pattern recognitionalgorithms or techniques to compare features or statisticaldistributions of features of the query image 224 with features 264 orstatistical distributions of features 264 of various reference sidingsamples 252.

Analysis engine 214 may thereby perform a supervised pattern recognitionanalysis. Such pattern recognition methods include two steps: trainingand testing. In the training stage, analysis engine 14 may collect“training” samples 252 and extract features 264 from such samples 252.Then analysis engine 214 may use pattern recognition techniques to learna pattern recognition function based on the distribution of features 264of the training data. In the testing step, analysis engine 214 may usethis function to estimate the likelihood of a query image 224 matchingvarious reference samples by analyzing the distribution of featuresextracted from the query image 224.

In embodiments in which different “patches” of the texture region areevaluated individually, analysis engine 214 may calculate a jointlikelihood of matching between the multiple patches and differentindividual reference siding samples or different texture groups, usingany suitable algorithms.

At step 330, analysis engine 214 may further analyze, through filtering,the level of similarity between the query sample and reference samplesbased on additional characteristic data 226 regarding the query sample(e.g., as received at step 306 of method 300 discussed above), using anysuitable algorithms. For example, analysis engine 214 may compareadditional characteristic data 226 regarding the query sample withcharacteristic data 270 in reference sample data sets 252 a-252 n tofilter the potentially similar reference samples. In some embodiments,analysis engine 214 may conclusively filter potentially similarreference samples based on the comparisons of one or more specifiedcategories of characteristic data 270, such that each reference samplethat does not match the classification of the query sample (e.g., theparticular material, style, face size, etc.) is excluded as a potentialmatch. In other embodiments, analysis engine 214 may consider theresults of the comparisons between query sample characteristic data andreference sample characteristic data 270, without conclusively filteringresults based on such comparison results.

For example, analysis engine 214 may apply a suitable algorithm thatfactors such comparison results into the calculated level of similaritybetween a query sample and reference sample, without necessarilyexcluding the reference sample if one or more characteristic dataclassifications do not match. Thus, analysis engine 214 may include analgorithm that processes the comparison of visual features 264 and thecomparison of characteristic data 270 as weighted factors to calculatean overall similarity score for each of a number of reference samples(as compared to the query sample).

After determining one or more samples that match the query sample,analysis engine 214 may output the one or more matching samples in anysuitable manner at step 332. For example, analysis engine 214 maydisplay or identify the best match (e.g., the reference sample havingthe highest similarity score) or multiple matching samples in order ofsimilarity (e.g., based on a determined score for each sample).

FIGS. 6A-6G are drawings showing example screenshots that might begenerated by an application for display on a user's mobile device,according to teachings of the present disclosure. For example, as shownin FIG. 6A, the insured may use a mobile device 510 to execute an app ora website for making a claim. Using the app/website, the user/insuredmay select “Claims Center” to initiate the making/filing of a claim. Atthe Claims Center, as shown in FIG. 6B, the insured is able to report anaccident, report damage, or select other relevant options. As shown inFIG. 6C, if the insured selectively inputs damage, the app/website willprompt him/her to select where the damage occurred (e.g., roof, siding,storm water, etc.). It may be appreciated that the user may be able toselect more than one location of the property (e.g., house, car,building, etc.) where the damage occurred.

Referring to FIG. 6D, once the location(s) of the damage is selected,the app/website will prompt the insured to take a photograph of thedamage using mobile device 510 (e.g., computer, smartphone, tablet,etc.). At FIG. 6E, the app/website will then allow the insured to cropor otherwise adjust the photo. As shown in FIG. 6F, based on the photo,the insured also may be able to input additional parameters, such asmaterial, face size, color, and/or style. The app/website will thengenerate a search results, as shown in FIG. 6G, and the insured is ableto best select the material choice that was damaged to complete thisreporting of information. If additional information is needed, theapp/website may ask for such information before completing the filing ofthe claim. Once the information is reported, the app/website may havefurther functions that provide information to a manufacturing facilityor other mechanism for obtaining the necessary materials to replace orrepair the damaged materials. As is apparent from the disclosure hereinand at least FIGS. 6A-6G, the insured is able to easily report anddocument damage merely using his/her mobile device 510.

FIG. 7 is a drawing illustrating an example system 500 for executingmethod 100 and other embodiments of the present disclosure, providingcommunication between a user device (e.g., mobile device 510) and otherexternal systems or devices, according to certain embodiments. As shown,mobile device 510 may be communicatively connected to one or more remoteserver(s) 522 and/or at least one remote data storage system(s) 524 viaone or more networks 526. Mobile device 510 may be used to display thevarious screens shown in FIGS. 6A-6G.

Servers 522 may include one or more components or devices operable toreceive data from mobile device 510 and further process and/or displaysuch data to the user via mobile device 510, personal digital assistants(PDA), laptop computers, desktop computers, or any other device. In someembodiments, a server 522 may include any suitable application(s) forinterfacing with mobile device 510, e.g., providing application(s) to bedownloaded via the Internet or otherwise installed on mobile device 510.

Remote data storage devices 524 may include any one or more data storagedevices for storing driving data received from mobile device 510 and/orservers 522. Remote data storage 524 may comprise any one or moredevices suitable for storing electronic data, e.g., RAM, DRAM, ROM,flash memory, and/or any other type of volatile or non-volatile memoryor storage device. Remote data storage device 524 may include anysuitable application(s) for interfacing with mobile device 510 and/orwith relevant applications running on servers 522.

Network(s) 526 may be implemented as, or may be a part of, a storagearea network (SAN), personal area network (PAN), local area network(LAN), a metropolitan area network (MAN), a wide area network (WAN), awireless local area network (WLAN), a virtual private network (VPN), anintranet, the Internet or any other appropriate architecture or systemthat facilitates the communication of signals, data and/or messages(generally referred to as data) via any one or more wired and/orwireless communication links.

In some embodiments, mobile device 510 may be used to request a repairfor damage to an insured automobile, residence, or building. Forexample, a user may engage mobile device 510 to access an insurancecompany's website, to send an email request, and/or any otherappropriate means of communicating with an insurance company. When aninsurance company receives a request for a repair, servers 522 maycommunicate various options back to the user through mobile device 510,such as providing the user an option to download an application formobile device 510 that would make repair requests and/or manage a repairprocess.

FIG. 8 is a drawing illustrating example components of mobile device 510for use with the teachings of the present disclosure. As shown, mobiledevice 510 may include a memory 530, processor 532, a display 536, andinput/output devices 538.

Memory 530 may store various applications to run or be executed byprocessor 532. Memory 530 may comprise one or more devices suitable forstoring electronic data, e.g., RAM, DRAM, ROM, internal flash memory,external flash memory cards (e.g., Multi Media Card (MMC), Reduced-SizeMMC (RS-MMC), Secure Digital (SD), MiniSD, MicroSD, Compact Flash, UltraCompact Flash, Sony Memory Stick, etc.), SIM memory, and/or any othertype of volatile or non-volatile memory or storage device.

Memory 530 may store various applications 544 which, when executed,direct the actions of processor 532. An application 544 may be describedin terms of functional modules 546 a, 546 b, 546 c, each embodied in aset of logic instructions (e.g., software code). For example, as shownin FIG. 8 , application 544 may include a data collection module 546 a,a data processing module 546 b, and a feedback module 546 c.

Processor 532 may include a microprocessor, a microcontroller, a digitalsignal processor (DSP), an application specific integrated controller(ASIC), electrically-programmable read-only memory (EPROM), or afield-programmable gate array (FPGA), or any other suitableprocessor(s), and may be generally operable to execute variousapplications, as well as supporting any other functions of mobile device510.

Display 536 may comprise any type of display device for displayinginformation related to a user, for example, an LCD screen (e.g., thinfilm transistor (TFT) LCD or super twisted nematic (STN) LCD), anorganic light-emitting diode (OLED) display, or any other suitable typeof display. In some embodiments, display 536 may be an interactivedisplay (e.g., a touch screen) that allows a user to interact withapplications running on processor 532. In other embodiments, display 536may be strictly a display device, such that all user input is receivedvia other input/output devices 538.

Input/output devices 538 may include any suitable interfaces allowing auser to interact with mobile device 510. For example, input/outputdevices 38 may include a camera, a touchscreen, physical buttons,sliders, switches, data ports, keyboard, mouse, voice activatedinterfaces, or any other suitable devices.

While this disclosure includes illustrative designs, such designs may befurther modified within the spirit and scope of this disclosure. Thisapplication is therefore intended to cover any variations, uses, oradaptations of the disclosure using its general principles. Further,this application is intended to cover such departures from the presentdisclosure as come within known or customary practice in the art towhich this disclosure pertains.

What is claimed:
 1. A method for generating material replacement, the method comprising: receiving, by a server, an image of a sample of an original material needing to be replaced; generating, by the server, representative data for the original material based upon the received image; accessing, by the server, a reference database that stores reference data for a plurality of different materials including reference texture data or reference characteristic data for each material of the plurality of different materials; comparing, by the server, the representative data for the original material to the reference data in the reference database to identify at least one material of the plurality of different materials that correlates with the original material; determining, by the server, if the at least one material of the plurality of different materials that correlates with the original material is not available in a needed amount and if the original material is aged beyond a predetermined threshold; sending, by the server, a request to generate a replacement material for the at least one material of the plurality of different materials that correlates with the original material; and generating the replacement material by manufacturing the replacement material.
 2. The method of claim 1, further comprising providing the replacement material for a partial replacement in an insured loss.
 3. The method of claim 1, wherein the original material includes at least one siding for a building, a roofing material, a window sill, a window trim, a plumbing fixture, a plumbing joint, a plumbing material, an automotive component, and an automotive material.
 4. The method of claim 1, wherein receiving the image includes receiving the image from a client or a claim representative of an insurance company.
 5. The method of claim 4, wherein the received image from the client or the claim representative of the insurance company is transmitted using a mobile device running an application for communication with the server.
 6. The method of claim 1, wherein the reference characteristic data include at least one of: a color, a texture, a construction material type, a construction material style, a construction material face characteristic, a construction material profile characteristic, and a construction material manufacturer name.
 7. The method of claim 1, further comprising receiving a request including an amount of replacement material needed.
 8. The method of claim 1, wherein sending the request to generate the replacement material includes sending the request to a custom manufacturing facility and sending a needed amount of the replacement material.
 9. The method of claim 8, wherein generating the replacement material by manufacturing the replacement material includes using an additive manufacturing process to manufacture the replacement material at the custom manufacturing facility.
 10. The method of claim 9, further comprising transmitting files for the additive manufacturing process of the replacement material from the reference database to the custom manufacturing facility.
 11. The method of claim 1, wherein the received image includes a digital image of a transferred ink pattern of the original material.
 12. The method of claim 1, wherein comparing the representative data for the original material to the reference data in the reference database to identify the at least one material of the plurality of different materials that correlates with the original material includes: determining a similarity score for the representative data based upon similarities to the reference data in the reference database; and identifying the at least one material of the plurality of different materials that correlates with the original material based upon the similarity score for the representative data.
 13. A system for generating a partial material replacement, the system comprising: a processor; a non-transitory storage medium; and a set of computer readable instructions stored in the non-transitory storage medium that, when executed by the processor, cause the processor to: receive an image of a sample of an original material needing to be replaced; generate representative data for the original material based upon the received image; access a reference database that stores reference data for a plurality of different materials including reference texture data or reference characteristic data for each material of the plurality of different materials; compare the representative data for the original material to the reference data in the reference database to identify at least one material of the plurality of materials that correlates with the original material; determine if the at least one material of the plurality of different materials that correlates with the original material is not available in a needed amount and if the original material is aged beyond a predetermined threshold; send a request to generate a replacement material for the at least one material of the plurality of different materials that correlates with the original material; and generate the replacement material by manufacturing the replacement material.
 14. The system of claim 13, wherein the set of computer readable instructions that cause the processor to generate the replacement material by manufacturing the replacement material further include instructions that cause the processor to use an additive manufacturing process to manufacture the replacement material.
 15. The system of claim 14, wherein the replacement material includes a veneer layered on an existing substrate.
 16. The system of claim 13, wherein the reference database stores design specifications for additive manufacturing of the plurality of different materials.
 17. A method for generating a partial material replacement, the method comprising: providing, by a server, a claims handling application to a user for installation on a client device of the user; collecting, by the server, claims data transmitted by the client device through the claims handling application; receiving, by the server, an image of a sample of an original material needing to be replaced; generating, by the server, representative data for the original material based upon the received image; accessing, by the server, a reference database that stores reference data for a plurality of different materials including reference texture data or reference characteristic data for each material of the plurality of different materials; comparing, by the server, the representative data for the original material to the reference data in the reference database to identify at least one material of the plurality of materials that correlates with the original material; determining, by the server, if the at least one material of the plurality of different materials that correlates with the original material is not available in a needed amount and if the original material is aged beyond a predetermined threshold; sending, by the server, a request to generate a replacement material for the at least one material of the plurality of different materials that correlates with the original material; and generating the replacement material by manufacturing the replacement material.
 18. The method of claim 17, wherein the replacement material is manufactured using at least one additive manufacturing process.
 19. The method of claim 18, wherein the request further includes design specifications for additive manufacturing of the replacement material. 